Many methods have been developed to secure the network infrastructure and communication over the Internet. Intrusion detection is a relatively new addition to such techniques. Intrusion detection systems (IDS) are used to find out if someone has intrusion into or is trying to get it the network. One big problem is amount of Intrusion which is increasing day by day. We need to know about network attack information using IDS, then analysing the effect. Due to the nature of IDSs which are solely signature based, every new intrusion cannot be detected; so it is important to introduce artificial intelligence (AI) methods / techniques in IDS. Introduction of AI necessitates the importance of normalization in intrusions. This work is focused on classification of AI based IDS techniques which will help better design intrusion detection systems in the future. We have also proposed a support vector machine for IDS to detect Smurf attack with much reliable accuracy.
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；Rotman School of Management, University of Toronto（多伦多大学罗特曼管理学院）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；Vector Institute（向量研究所）；NVIDIA Deep Learning Institute（NVIDIA 深度学习学院）
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
MetaMind Innovation P.C.（MetaMind创新有限公司）；University of Western Macedonia（西马其顿大学）；K3Y Ltd（K3Y有限公司）；Democritus University of Thrace（色雷斯德谟克利特大学）；Kingston University London（伦敦金斯顿大学）
Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)
Biometrics and Data Pattern Analytics Lab, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, 28049, Madrid, Spain（生物特征与数据模式分析实验室，西班牙马德里自治大学坎托布兰科大学城）
Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework
Department of Computer Science and Engineering, United International University (UIU), Dhaka 1212, Bangladesh（计算机科学与工程系，国际联合大学（UIU），达卡1212，孟加拉国）；Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh（电气与电子工程系，伊斯兰科技大学，加兹ipur 1704，孟加拉国）；Department of Computer Science and Engineering (CSE), University of Asia Pacific (UAP), Dhaka 1207, Bangladesh（计算机科学与工程系（CSE），亚洲太平洋大学（UAP），达卡1207，孟加拉国）；Department of Information Systems, University of Maryland, Baltimore, 21250, Maryland, USA（信息系统系，马里兰大学，巴尔的摩，21250，美国）；College Of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA（信息科学与技术学院，宾夕法尼亚州立大学，大学公园，PA 16802，美国）；Department of Information Technology, Washington University of Science and Technology, Alexandria, VA（信息技术系，科学与技术华盛顿大学，亚历山大，VA）；College of Engineering and Information Technology, University of Maryland, College Park, 20742, Maryland, USA（工程与信息技术学院，马里兰大学，学院公园，20742，美国）
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning
Rabah Rahal Networks and Systems Laboratory (LRS), Badji Mokhtar Annaba University, Algeria（拉比·拉尔实验室，巴吉·莫克塔尔安纳巴大学，阿尔及利亚）；Abdelaziz Amara korba Networks and Systems Laboratory (LRS), Badji Mokhtar Annaba University, Algeria（阿卜杜勒阿齐兹·阿马拉·科尔巴实验室，巴吉·莫克塔尔安纳巴大学，阿尔及利亚）；L3I, University of La Rochelle, France（L3I，拉罗歇尔大学，法国）
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（电气工程与计算机科学学院（SEECS），国家 sciences and Technology（NUST））
